Sajjad Shafiei presented three research papers from CityX members at the IEEE ITSC 2016 held in Rio, November 1-4.
Calibration of Traffic Flow Fundamental Diagrams for Network Simulation Applications: A Two-Stage Clustering Approach
Gu, Z., Saberi, M., Sarvi, M., Liu, Z.
Abstract. This paper aims to propose a two-stage clustering approach for calibration of traffic flow fundamental diagrams for dynamic traffic assignment (DTA) simulations. Unlike previous research efforts focusing on supervised grouping strategies that are largely dependent on roadway physical attributes, a da-ta-driven perspective is explored using big traffic data. The two-regime modified Greenshields traffic flow model is used to fit the historical observations on a daily basis using the non-linear least squares method. A two-stage clustering ap-proach is proposed based on the calibrated models where the first stage aims to capture day-to-day variations in traffic flow fundamental diagrams while the second stage aims to aggregate links with similar traffic flow characteristics. The standard k-means algorithm is applied in the first stage and a modified hierarchical clustering based on the Fréchet distance is pro-posed in the second stage. The calibrated and clustered results highlight the feasibility and the effectiveness of the proposed approach.
Application of an Exact Gradient Method to Estimate Dynamic Origin-Destination Demand for Melbourne Network
Shafiei, S., Saberi, M., Sarvi, M.
Abstract. The time-dependent origin-destination (TDOD) demand estimation problem aims at estimating dynamic demand that represents the observed traffic flow patterns in a transportation network. Errors in TDOD demand are often propagated into the network outputs causing unreliable planning and operational policies. In this study, a bi-level optimization problem is proposed where the upper level is an Ordinary Least-Squared (OLS) error minimization problem that minimizes the deviation between the estimated and observed traffic volumes from SCATS, while the lower level generates assignment proportions matrix using a mesoscopic simulation-based dynamic user equilibrium model. The interior point conjugate gradient method, as an exact gradient method, is applied to solve the TDOD demand estimation problem. The obtained results highlight the capability of the proposed approach in improving the performance of a dynamic large-scale network model of Melbourne CBD.
A Solution to the Road Network Design Problem for Multimodal Flow
Asadi, S., Sarvi, M., Rajabifard, A., Thompson, R., Saberi, M.
Abstract. Given a set of candidate road projects associated with costs, finding the best subset with respect to a limited budget is known as the discrete network design problem (DNDP). The DNDP is often cast in a bilevel programming problem which is known to be NP-hard. Despite a plethora of research, due to the combinatorial complexity, efforts to address the problem for large-sized networks while considering public transport as well as other modes (multimodal/multiclass traffic flow) are scarce. To this end, we first turn the bilevel problem to a single-level problem based on System-Optimal traffic flow which results in a mixed integer non-linear programming (MINLP) problem. Second, we develop an efficient Benders decomposition algorithm to solve the ensuing MINLP problem. The multiclass/multimodal features of the traffic flow are ensured by employing the Spiess’ bias term and optimal strategy methods. The proposed methodology is applied to Sioux Falls and a real sized network of the city of Winnipeg, Canada.
Dr. Meead Saberi, lecturer in transportation engineering, data guru, and urban scientist